Robust Iterative Learning Control of 2-D Linear Discrete FMMII Systems Subject to Iteration-Dependent Uncertainties

In this article, robust and convergent properties of iterative learning control (ILC) laws are investigated for two kinds of two-dimensional (2-D) linear discrete systems (LDS) in the second Fornasini-Marchesini's model (FMMII). The iteration-dependent uncertainties in reference trajectories, boundary states, and disturbances are considered in the ILC designs of 2-D FMMII. By virtue of the lifting matrix/vector technique, the ILC processes of two kinds of 2-D FMMII are transformed into 2-D linear inequalities. As a result, robust and convergent conditions are obtained for the proposed ILC laws. It is theoretically proved that by using the proposed ILC laws, under the discussed iteration-dependent uncertainties, the ILC tracking errors of 2-D FMMII can be driven into a residual range, the bound of which is relevant to the bound parameters of uncertainties. In particular, when the iteration-dependent uncertainties of 2-D FMMII are progressively convergent in iteration domain, an accurate tracking to the desired reference trajectory can be realized except at the boundaries. Numerical simulations are used to illustrate the validity and feasibility of the designed ILC laws.